The authors generate and publicly release the first large-scale open dataset of three million structured moral fables produced by small open language models together with a reproducible LLM-judge evaluation pipeline.
Hwang, Maxwell Forbes, and Yejin Choi
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
Output prefilling with a structured prefix steers LLMs to produce cleaner first tokens in MCQA, raising accuracy and calibration over standard first-token probability.
citing papers explorer
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TF1-EN-3M: Three Million Synthetic Moral Fables for Training Small, Open Language Models
The authors generate and publicly release the first large-scale open dataset of three million structured moral fables produced by small open language models together with a reproducible LLM-judge evaluation pipeline.
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Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
Introduces a gradient-based multilingual audit framework for LLM moral decisions in robot assistance scenarios and reports persistent culturally asymmetric gradient tracking failures not fixed by prompting.
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Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Output Prefilling
Output prefilling with a structured prefix steers LLMs to produce cleaner first tokens in MCQA, raising accuracy and calibration over standard first-token probability.